Paper Info
Title
That'S Not What I Meant! Using Parsers To Avoid Structural Ambiguities In Generated Text
Abstract
We investigate whether parsers can be used for self-monitoring in surface realization in order to avoid egregious errors involving "vicious" ambiguities, namely those where the intended interpretation fails to be considerably more likely than alternative ones. Using parse accuracy in a simple reranking strategy for self-monitoring, we find that with a state-of-the-art averaged perceptron realization ranking model, BLEU scores cannot be improved with any of the well-known Treebank parsers we tested, since these parsers too often make errors that human readers would be unlikely to make. However, by using an SVM ranker to combine the realizer's model score together with features from multiple parsers, including ones designed to make the ranker more robust to parsing mistakes, we show that significant increases in BLEU scores can be achieved. Moreover, via a targeted manual analysis, we demonstrate that the SVM reranker frequently manages to avoid vicious ambiguities, while its ranking errors tend to affect fluency much more often than adequacy.
Year
Venue
Field
2014
PROCEEDINGS OF THE 52ND ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, VOL 1
Ranking,Fluency,Computer science,Support vector machine,Treebank,Artificial intelligence,Natural language processing,Parsing,Perceptron,Machine learning
DocType
Volume
Citations 
Conference
P14-1
2
PageRank 
References 
Authors
0.40
21
2
Name
Order
Citations
PageRank
Manjuan Duan150.82
Michael White2518.25